【摘要】：波動(dòng)性是衡量金融市場質(zhì)量和效益的指標(biāo),若能精確地捕捉到金融時(shí)間序列的波動(dòng)性特征,就可以極大的提高金融市場的流動(dòng)性和資源配置。在經(jīng)濟(jì)全球化的今天,信息是連續(xù)影響證券市場價(jià)格的運(yùn)動(dòng)過程。數(shù)據(jù)的離散采集直接影響到獲取市場信息的多少,數(shù)據(jù)采集頻率越高,獲取的市場信息也就越多。高頻數(shù)據(jù)是對(duì)金融市場感興趣的人們的主要研究對(duì)象,尤其是交易人員通過觀察高頻或逐點(diǎn)數(shù)據(jù)來確定交易決策。高頻數(shù)據(jù)的極值序列是不同頻率的數(shù)據(jù)在每一段時(shí)間間隔內(nèi)取極大值和極小值而形成的兩個(gè)時(shí)間序列,它與以往研究的時(shí)間序列有所不同,能夠使我們更準(zhǔn)確的分析極端市場的條件和信息對(duì)證券市場產(chǎn)生的影響,這也是本文的創(chuàng)新點(diǎn)。研究高頻數(shù)據(jù)以及收益率的統(tǒng)計(jì)特征,并刻畫市場信息與極值及其收益率之間的波動(dòng)性,對(duì)我國的證券市場進(jìn)行理論和實(shí)證分析,具有非常重要的理論和實(shí)際意義。 本文主要討論了基于GARCH模型高頻數(shù)據(jù)極值的波動(dòng)性研究,選取中國股市中的滬深300指數(shù)15分鐘收益數(shù)據(jù)的極大值和極小值為研究對(duì)象,運(yùn)用GARCH模型建模分析。第一章主要是介紹了國內(nèi)外關(guān)于波動(dòng)性的研究現(xiàn)狀。第二章系統(tǒng)的介紹了波動(dòng)性計(jì)量模型的基本理論和波動(dòng)性的主要特征,并從理論上進(jìn)行各類模型間的比較,以初步揭示各個(gè)模型的不同特點(diǎn)。第三章主要運(yùn)用GARCH模型對(duì)收益序列進(jìn)行了建模,對(duì)波動(dòng)性的波動(dòng)聚集性和尖峰厚尾性及波動(dòng)的非對(duì)稱性進(jìn)行了分析。結(jié)論表明,滬深股市具有明顯的波動(dòng)性,收益率數(shù)據(jù)自身存在尖峰厚尾性,波動(dòng)聚集性,且服從非正態(tài)分布。最后總結(jié)本文的分析結(jié)果,對(duì)模型提出了改進(jìn)。
[Abstract]:Volatility is an index to measure the quality and benefit of financial market. If we can accurately capture the volatility characteristics of financial time series, we can greatly improve the liquidity and resource allocation of financial market. In the economic globalization today, the information is the movement process which affects the stock market price continuously. Discrete data acquisition directly affects the amount of market information, the higher the frequency of data acquisition, the more market information will be obtained. High frequency data is the main research object of people who are interested in financial market, especially traders determine trading decision by observing high frequency or point by point data. The extreme value sequence of high frequency data is two time series formed by taking maximum value and minimum value of different frequency data in each time interval, which is different from previous time series. It can make us more accurate analysis of the extreme market conditions and the impact of information on the securities market, which is also the innovation of this paper. It is of great theoretical and practical significance to study the statistical characteristics of the high frequency data and the return rate and to describe the volatility between the market information and the extreme value and the rate of return. The theoretical and empirical analysis of the securities market in China is of great theoretical and practical significance. This paper mainly discusses the volatility research of high frequency data based on GARCH model, selects the maximum and minimum of 15-minute income data of CSI 300 index in Chinese stock market as the research object, and uses GARCH model to model and analyze. The first chapter introduces the current situation of volatility research at home and abroad. The second chapter systematically introduces the basic theory of volatility econometric model and the main characteristics of volatility, and carries on the theoretical comparison among various models, in order to reveal the different characteristics of each model. In the third chapter, the GARCH model is used to model the return series, and the volatility aggregation, the spike and the thick tail and the asymmetry of volatility are analyzed. The conclusion shows that the stock market in Shanghai and Shenzhen has obvious volatility, and the data of yield has its own peak and thick tail, and the volatility is concentrated, and the non-normal distribution of the stock market is not normal. Finally, the analysis results of this paper are summarized, and the improvement of the model is put forward.
【學(xué)位授予單位】：長春工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：O211.61;F830.9

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 張波,李綱,倪娜;SV模型在我國股市風(fēng)險(xiǎn)度量中的應(yīng)用[J];商業(yè)研究;2004年19期

2 劉金全;崔暢;;中國滬深股市收益率和波動(dòng)性的實(shí)證分析[J];經(jīng)濟(jì)學(xué)(季刊);2002年03期

3 宮汝凱;;我國股市ARCH效應(yīng)的實(shí)證研究[J];金融與經(jīng)濟(jì);2008年12期

4 阮慧敏;謝雋;;中國滬深股市收益率及波動(dòng)性實(shí)證研究[J];科教文匯(下半月);2006年12期

5 周恒,殷瑞飛;深圳股票市場波動(dòng)率估計(jì)和預(yù)測(cè)[J];市場周刊(研究版);2005年01期

6 劉鳳芹,吳喜之;基于SV模型的深圳股市波動(dòng)的預(yù)測(cè)[J];山西財(cái)經(jīng)大學(xué)學(xué)報(bào);2004年04期

7 魯萬波;;參數(shù)與非參數(shù)GARCH模型的預(yù)測(cè)能力比較[J];統(tǒng)計(jì)與決策;2006年23期

8 張歡;蔣佐斌;陳楊林;;運(yùn)用GARCH模型對(duì)CRB能源價(jià)格指數(shù)序列的擬合與預(yù)測(cè)[J];統(tǒng)計(jì)與決策;2007年22期

9 沈豪杰;黃峰;;中國股市流動(dòng)性風(fēng)險(xiǎn)的非對(duì)稱效應(yīng)分析[J];統(tǒng)計(jì)與信息論壇;2010年04期

10 陳學(xué)華,楊輝耀;APARCH模型在證券投資風(fēng)險(xiǎn)分析中的應(yīng)用[J];運(yùn)籌與管理;2003年03期

，

本文編號(hào)：2204080